Evidence of structural segmentation of the Uttarakhand Himalaya and its implications for earthquake hazard

The earthquake hazard associated with the Main Himalayan Thrust (MHT) is a critical issue for India and its neighbouring countries in the north. We used data from a dense seismic network in Uttarakhand, India, to model the lateral variations in the depths of MHT (2–6% drop in Vs at 12–21 km depths), Moho (a sharp increase in Vs (by ~ 0.5–0.7 km/s) at 39–50 km depths) and lithosphere (a marked decrease in Vs(~ 1–3%) at 136–178 km depths), across the Himalayan collisional front. Our joint inversion of radial PRFs and group velocity dispersion data of Rayleigh waves detects three NNE trending transverse lithospheric blocks segmenting the lithosphere in Uttarakhand Himalaya, which spatially correlate well with the northward extension of the Delhi -Haridwar Indian basement ridge, an inferred tectonic boundary and great boundary fault, respectively. Our radial receiver function imaging detects highly deformed and segmented crustal and lithospheric structures associated with three mapped transverse lithospheric blocks, suggesting a reduction in rupture lengths of future earthquakes, thereby, reducing earthquake hazards in Uttarakhand.

). Each station is equipped with a 24-bit 120s Reftek 3-component broadband sensor and a GPS clock for time tagging. This network has recorded several hundred good regional and teleseismic events during 2017-2021. Here, we utilized the dataset from the above network to jointly invert the radial PRFs and fundamental mode group velocity dispersion data of Rayleigh waves to estimate thicknesses of MHT, crust and lithosphere in the Uttarakhand Himalaya. A high resolution 24-bit Reftek-130 recorder and a Reftek broadband sensor are used at each station, with a GPS time tagging. The continuous broadband data at each station is recorded at 50 samples per second.
The seismographs were located on hard rock sites for achieving higher signal to noise ratio.
During February 2017 -November 2021, 200 good teleseismic earthquakes were recorded by 45 out of 56 seismographs from the above network . To estimate PRFs, first a wavefrom from -5 to 60 s is considered from the 3-component broaband data, which is corrected from the instrumental correction. Then, all the windowed waveforms are filtered using a high pass filter with a corner frequency of 0.03 Hz before the computation of PRFs.
For the present study, we used the above-mentioned dataset to compute radial and transverse  (Fig.1a), to conduct the joint inversion 2 study using radial P-RFs and fundamental mode group velocity dispersion data of Rayleigh waves, to estimate MHT thickness, Moho depths and lithosphere thicknesses in the Uttrakhand Himalayan region (see supplementary Table S1; Figs. 2-3, see Supplementary Figs. S11-S53). Due to the uneven azimuthal distribution of teleseismic events (Fig. 1c), reliable anisotropic structures are difficult to retrieve with this dataset, and so we invert only for isotropic crustal structure. For surface wave tomography, Saha et al. 3 have used interpolation based on the three closest neighbours, and nodes are separated at nearly constant distances from one another. This is done by constructing Delaunay triangles from the set of nodes on the sphere. Each triangle formed by the three nodes at the plane's vertices, which is almost the tangent plane to the sphere. Three-point linear interpolation is used to evaluate the model within each Delaunay triangle. Rayleigh wave dispersion data are extracted from previous tomographic study 45 that provides group velocity data in the period of 10s to 100s at 0.5° interval. The group velocity map was generated using ambient noise and earthquake data recorded over a network of 683 board band seismic stations in India, Tibet and surrounding regions. A representative group velocity map of the region at 10s, 50s, and 100s time period is presented in the supplementary Fig.S9. Also, we provide results of a checkerboard resolution test for periods 10s and 100s in the supplementary Fig.S10. Saha et al. 3 generated resolution test with square cells of size varying from 0.5° to 2° and found 1°×1° to be the optimum. Tomographic images were constructed by them at 10-100 s periods using path averaged group velocity measurements at 1• × 1• grid cells. We extracted surface wave group velocity dispersion data at 10 -100s periods for each of our station from the above discussed tomograms of Saha et al. 3 . For joint inversion study, three different influence parameters viz., 0.3, 0.4 and 0.5 have been used to perform the joint inversion for selecting the correct influence parameter. After this examination, an influencing parameter of 0.3 is chosen for the joint inversion study. Next, at each station the joint inversion is performed for three different damping parameters, i.e., 0.4, 0.6 and 1.0, for examining the performance of each value of the influence parameter. For a damping parameter of 1, the RMS velocity model perturbation and estimated data standard deviation are found to be minimum. The inversion scheme provided a stable solution for different stations after 40 iterations, with damping of 1.0 and an influence parameter of 0.3 (see supplementary Table S1). Thus, our final model is more influenced by PRFs than the regional surface wave group velocity dispersion data, which have been constructed from the regional tomograms 3 . Here, we perform 40 iterations of inversion for different stations to obtain the final model at 45 broadband stations (Figs. 2-3; also see Supplementary Figs. S11-S53). When we ran the joint inversion, we could fit the surface waves without significantly degrading the fit to the receiver functions. The joint inversion is stopped only after obtaining the best fit Vs model showing good correlation ( 85%) between the all available observed and inverted P-RFs (over the available range of horizontal slowness and back-azimuths at one station) and fundamental mode group velocity dispersion data of Rayleigh waves. The same procedure of joint inversion is performed to estimate the best-fit 1-D shear velocity model for all 45 broadband stations (down to a depth of 200 km) in the Uttarakhand Himalayan region and surface-wave dispersion observations. Geophys. J. Int. 143, 99-112, doi 10.1046Int. 143, 99-112, doi 10. /j.1365Int. 143, 99-112, doi 10. -246x.2000Int. 143, 99-112, doi 10. .00217.x (2000.
3. G.K. Saha, K.S. Prakasam, S.S. Rai, Diversity in the peninsular Indian lithosphere revealed from ambient noise and earthquake tomography. Phys, Earth and Planet. Interiors 306, 106523, 1-17 (2020 Figure S10: Results obtained from a checkerboard resolution test 3 of the Rayleigh wave tomography for the Uttarakhand region, for periods 10s and 100s. Fig S11: Results of joint inversion of P-RFs and fundamental mode surface wave group velocity dispersion (SWD) data at AUGM station, (a) showing good agreement between observed (black line) and inverted (red line) radial RFs with a=1.0, 1.5 and 2.0, for different horizontal slowness (S, in s/km). Here, "a" and "R%" represent Gaussian width factor (used for estimating RF) and agreement (in %) between observed and inverted RFs, respectively. Correlation between observed and inverted dispersion curves of (b) Rayleigh waves.   Results of joint inversion of P-RFs and fundamental mode surface wave group velocity dispersion (SWD) data at TEH station, (a) showing good agreement between observed (black line) and inverted (red line) radial RFs with a=1.0, 1.5 and 2.0, for different horizontal slowness (S, in s/km). Here, "a" and "R%" represent Gaussian width factor (used for estimating RF) and agreement (in %) between observed and inverted RFs, respectively. Correlation between observed and inverted dispersion curves of (b) Rayleigh waves. (c) Inverted shear velocity models showing the main Himalayan thrust (MHT), Moho (M) and LAB (L) depth estimates in km. Different colours represent different Vs models used for the joint inversion. The initial shear velocity model is shown by a thick red dotted line, while the final shear velocity model is shown by a thick blue line and (d) A plot showing zoomed portion of the crustal part from the inverted Vs models. Fig S52: Results of joint inversion of P-RFs and fundamental mode surface wave group velocity dispersion (SWD) data at THN station, (a) showing good agreement between observed (black line) and inverted (red line) radial RFs with a=1.0, 1.5 and 2.0, for different horizontal slowness (S, in s/km). Here, "a" and "R%" represent Gaussian width factor (used for estimating RF) and agreement (in %) between observed and inverted RFs, respectively. Correlation between observed and inverted dispersion curves of (b) Rayleigh waves. (c) Inverted shear velocity models showing the main Himalayan thrust (MHT), Moho (M) and LAB (L) depth estimates in km. Different colours represent different Vs models used for the joint inversion. The initial shear velocity model is shown by a thick red dotted line, while the final shear velocity model is shown by a thick blue line and (d) A plot showing zoomed portion of the crustal part from the inverted Vs models. Fig S53: Results of joint inversion of P-RFs and fundamental mode surface wave group velocity dispersion (SWD) data at TUN station, (a) showing good agreement between observed (black line) and inverted (red line) radial RFs with a=1.0, 1.5 and 2.0, for different horizontal slowness (S, in s/km). Here, "a" and "R%" represent Gaussian width factor (used for estimating RF) and agreement (in %) between observed and inverted RFs, respectively. Correlation between observed and inverted dispersion curves of (b) Rayleigh waves.    Fig.2a. Double Moho structure, which is shown by white dotted lines and marked by M1 and M2, is also mapped below the epicentral zone of the 1999 Chamoli earthquake along the BB' profile.